In networked data technologies, the introduction and expanded use of middleware platforms has enabled a wider range of applications access to a wider range of data sources. Middleware platforms in general are known which allow a single application to access diverse or incompatible data sources, by commonizing the data schema used by those separate sources for use by the application.
In the realm of data analysis, online analytic processing (OLAP) applications are known which are configured to receive data in standardized formats. OLAP applications in general manipulate data which is stored in a multi-dimensional format, and manipulate multi-dimensional data to generate reports, statistics, and other outputs. Middleware platforms which attempt to combine multiple data sources for purposes of feeding data to one or more OLAP application can encounter difficulties, however. Those issues include the fact that the diverse data sources will typically not be built with completely common or consistent dimensions. An OLAP application which wishes to attempt, for example, a sort of multi-dimensional data that is merged from multiple sources may find it impossible to locate the desired data at the correct dimension or hierarchy within the combined set of data. Thus, for example, an OLAP application configured to analyze sales data may not be able to sort sales of a given item on a given date, if for example date is not an explicit dimension of all data sources. It may be desirable to provide methods and systems for the hierarchical aggregation of data sources which permit diverse data sources having different defined dimensions to be mapped to or combined on common dimensions for OLAP or other uses, while preserving the data of the original data sources.